03445ntm a2200337 i 4500 MY-KuUP 20260727153245.0 t||||fr|||| 000 0 ta 260727t20262026my a|||fr|||| 000 0 eng d THE0009494 (Local) Hardback UMPSA eng UMPSA rda FTKEE .M34 2026 r Thesis Md Mahabub Alam, author. Machine learning-based predictive modelling for bandwidth optimization of circular siw antennas in the K/KA band / Md Mahabub Alam Kuantan, Pahang : UMPSA, 2026 © 2026 xiii, 155 pages : illustrations ; 30 cm. + 1 CD-COM. rdacontent text rdamedia unmediated rdacarrier volume rda text file PDF Faculty of Electrical & Electronics Engineering Thesis (Master of Science) -- Universiti Malaysia Pahang - 2026 Includes bibliographical references Machine Learning (ML) has emerged as a powerful tool in antenna engineering, enabling efficient optimization, accurate electromagnetic (EM) modelling, and rapid performance prediction with significantly reduced computational cost. This thesis presents the implementation and validation of an efficient ML-based framework for predicting the impedance bandwidth of circular substrate-integrated waveguide (SIW) antennas operating in the millimetre-wave(mm-wave) spectrum. The primary objective is to overcome the high computational cost and time-consuming nature of conventional fullwave EM simulation-based antenna design, particularly for K- and Ka-band applications requiring extensive parametric optimization. A triple-band circular SIW antenna was designed and optimized using CST Microwave Studio, achieving resonant frequencies at 26.176 GHz, 31.6 GHz, and 33.59 GHz, with an impedance bandwidth of 2.52 GHz covering 24.885-27.405 GHz. The antenna’s robustness was verified through crossplatform EM simulations using CST, HFSS, and ADS. A comprehensive simulationbased dataset was then generated by systematically varying key geometrical parameters affecting bandwidth. Statistical analysis confirmed uniform data distribution and suitability for ML training. Six classical regression models and an artificial neural network (ANN) were developed to predict antenna bandwidth directly from geometrical parameters. The ANN outperformed all other models, achieving a testing coefficient of determination (R²) of 93.95%. Further validation using 5-fold cross-validation yielded average metrics of MAE = 0.0411, MSE = 0.00448, RMSE = 0.0631, and R² = 0.8527, demonstrating strong generalization and robustness. Additional testing on 15 unseen antenna configurations showed close agreement between ML predictions and full-wave EM simulation results. The proposed ML-based framework significantly reduces computational overhead and design iteration time, enabling rapid bandwidth estimation and efficient exploration of the antenna design space. These results confirm the framework’s effectiveness as a practical tool for bandwidth optimization and parametric analysis of circular SIW antennas in K/Ka-band applications. Faculty of Electrical & Electronics Engineering Dissertations Universities and colleges Dissertations Thesis Dissertations lcc THESIS 0 0 lcc 0 -2 NFIC 20000 20000 2026-07-27 0 FTKEE .M34 2026 r Thesis T000005070 2026-07-27 15:33:45 1 2026-07-27 THESIS 0 0 lcc 0 -2 NFIC 20000 20000 2026-07-27 0 CD14032 T000005071 2026-07-27 15:34:22 1 2026-07-27 ACCOM 105318 105324